Dear LFMM team.
I've been trying to use LFMM with my data, and for that I have tried to follow the tutorial you have provided.
Despite being rather complete, in the practical aspect, I am currently having some trouble with the interpretation.
My data is similar to that you present with A. thaliana, as it is a set of SNP markers where I am attempting to find associations with a set of environmental variables.
Here are my questions:
When testing multiple environmental variables, should I correct the calibrated.pvalue with an FDR test? Or can the tests be considered independent between environmental variables?
When you show the p-values in a "Manhattan plot", you highlight some of the SNPs as "causal". I suppose you knew this a priori and thus, have such a list. If these were already known to be "causal" loci, how come so many of them are above the significance threshold? Is this expected?
Best regards,
Francisco
PS - Apologies if this is not the best channel to place these questions. In which case, could you please point me towards the preferred method of asking them.
Dear LFMM team. I've been trying to use LFMM with my data, and for that I have tried to follow the tutorial you have provided. Despite being rather complete, in the practical aspect, I am currently having some trouble with the interpretation. My data is similar to that you present with A. thaliana, as it is a set of SNP markers where I am attempting to find associations with a set of environmental variables. Here are my questions:
calibrated.pvaluewith an FDR test? Or can the tests be considered independent between environmental variables?Best regards,
Francisco
PS - Apologies if this is not the best channel to place these questions. In which case, could you please point me towards the preferred method of asking them.